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Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies
Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 578–598
A. James O’Malley   Xin Ran   Chuankai An     All authors (4)

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https://doi.org/10.6339/22-JDS1064
Pub. online: 30 August 2022      Type: Data Science In Action      Open accessOpen Access

Received
22 March 2022
Accepted
15 August 2022
Published
30 August 2022

Abstract

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of “referral paths” – sequences of patient-specific temporally linked physician visits – in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital’s adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Supplementary material

 Supplementary Material
In the supplemental appendix, which accompanies this manuscript and will be published on the journal website in the “Supplementary Material” section, we present expanded descriptions of the data wrangling and additional results that were not able to be included in the main paper due to length restrictions. The results in the supplemental appendix are supported by the text that would have accompanied them in the main text had space permitted. In addition, we also refer readers to the following GitHub site to obtain R and Python code used in the analyses: https://github.com/kiwijomalley/OptimalBipartiteProjection.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Open access article under the CC BY license.

Keywords
bipartite network directional information factorial design implantable cardiac defibrillators optimal bipartite-unipartite projection shared-patient physician network

Funding
This work was supported by the National Institutes of Health in the USA (grant number P01 AG019783).

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